The present invention relates to a color image processing method and device thereof, and more particularly, to a color image processing method and apparatus which converts the output signal of a color image input device into an intermediate color space, divides regions in accordance with error characteristics in the transformed intermediate color space and re-corrects the color.
Generally, in a color image input device which converts image information into an electrical signal using a light-to-current converter, i.e., an image sensor like a charge-coupled device, the electrical signal output is usually divided into three colors (red, green and blue) which are used to represent the color information of the image. When this color image information is represented by the integration of the red, green and blue electrical signals, various devices output a variety of values with respect to the same image, depending on the particular design of an image input device by a given manufacturer. In this case, when the image is re-produced via an image output device using the above output value, each reproduced image is reflected by the characteristics of each image input device excluding the distortion of the image output device. As a result, the original image is not correctly expressed. This can be an even more serious problem under a network environment where various image input and output devices are employed simultaneously.
To solve the problem, a method has been introduced for reducing the characteristic errors of image input device and image output device by identifying the relationship between the spectrum of the input device and the intermediate coordinates and the relationship between the spectrum of output device and the intermediate coordinates, based on new intermediate coordinates. Here, the color space (CIEXYZ/L*a*b and so on) proposed by the Commission Internationale de l'Eclairage (CIE) is generally used as the intermediate coordinates. However, a conversion error is generated in the course of transform, and a large amount of time and memory capacity is consumed.
As conventional representative methods wherein the output signal of the image input device is converted into the value of the intermediate coordinate field (color space), there are transform methods by a model and by a look-up table (LUT).
The transform method by a model is one in which a model is assumed and the variable of the model is optimized based on physical measurements under specified conditions. A typical model is in the form of a matrix and, here, a recurrent analysis is commonly used for optimizing the variable of the model. Currently used models include: ##STR1##
Here, model 1 is a linear transform (3.times.3 matrix), model 2 is a square transform (3.times.6 matrix), and model 3 is a cross-product and square transform (3.times.9 matrix). The constituting of models in addition to the above three is possible. However, in doing so, the increase of the non-linear column makes the calculation more complicated, which reduces its practicality.
The variable value of the model can be calculated as follows. First, the output signals R, G and B of a scanner are obtained with respect to selected color samples. Second, the value of the color space CIEXYZ or L*a*b with respect to the color samples is obtained via an estimation under specific conditions. Third, the variable value of the model (transform ln matrix) is obtained by a recurrent analysis.
A color image processor is shown in FIG. 1, which converts the output signals R, G and B of a scanner into a color space signal using a transform matrix (TM) having the thus-obtained variable values.
When the characteristic of the output spectrum of the scanner is in accordance with the color matching function (the Luther condition), the output signal of the scanner can be converted into the value of the color space by a 3.times.3 linear transform matrix. However, when the output of scanner is transformed in color space value by using model 1 only, the conversion deviation is more serious than the case where non-linear column is included, since no scanner satisfies the Luther condition. Moreover, when the non-linear column increases, as in models 2 and 3, the time required for the exact transform also increases.
As models having more and more non-linear columns are applied, the number of color samples used for obtaining TM must also increase. In this case, when the variable value of the model is calculated using a few color samples, expressing the wide color region is more inappropriate than the case where model 1 is used.
The transform method by a look-up table is as follows. One to one correspondence relationship between the value of the color space with respect to the color sample and the R, G and B signals, i.e., an output signal of the scanner, is obtained using a lot of color samples. Then, based on the thus-obtained value, R, G and b color coordinates space of the scanner is divided into a regular matrix form using the interpolation and extrapolation methods. And, the value of the color space corresponding to each point is stored into a memory device for future use.
The transform look-up table can be obtained as follows. First, the color space value (CIEXYZ or L*a*b) of the selected color sample is obtained using the colorimetry device tinder the specific condition. Second, the output signals R, G and B of the selected color sample are obtained. Third, the scanner output signal value and the color space value are interpolated so as to increase their corresponding point. Fourth, the scanner output signal value and the scope of the color space is divided into the desired matrix point (for example, 33.times.33.times.33.times.6 tetrahedron or cubic). Fifth, an action that the color space value corresponding to a predetermined matrix point within the gamut of the scanner signal is found and stored in the table is repeatedly performed. Sixth, if the desired matrix point does not exist within the gamut, the corresponding color space value is obtained and stored into the look-up table using extrapolation method.
A color image processor which converts the output of the scanner, i.e., R, G and B signals, into the signal of the color space using the thus-obtained transform look-up table, is shown in FIG. 2.
Though the look-up table transform method has the least conversion deviation among the conventional methods, the memory device is consumed much. In more detail, a look-up table with respect to the matrix point of 33.times.33.times.33 requires at least a 200 Kb memory device, even though the color space value within the look-up table is stored as a constant type, and when the color space value in the look-up table is stored in the form of the real number, at least 400K bytes of memory is needed. Moreover, calculating the interpolation requires an excessive amount of time.